CN111580561A - Unmanned aerial vehicle scheduling method and system based on particle swarm optimization and readable storage medium - Google Patents
Unmanned aerial vehicle scheduling method and system based on particle swarm optimization and readable storage medium Download PDFInfo
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- CN111580561A CN111580561A CN202010481908.4A CN202010481908A CN111580561A CN 111580561 A CN111580561 A CN 111580561A CN 202010481908 A CN202010481908 A CN 202010481908A CN 111580561 A CN111580561 A CN 111580561A
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- G05D1/104—Simultaneous control of position or course in three dimensions specially adapted for aircraft involving a plurality of aircrafts, e.g. formation flying
Abstract
The invention discloses an unmanned aerial vehicle scheduling method, an unmanned aerial vehicle scheduling system and a readable storage medium based on particle swarm optimization, wherein the method comprises the following steps: the control center sends a task preparation instruction to the standby unmanned aerial vehicle group; after receiving a task preparation instruction, initializing a machine body parameter by the unmanned aerial vehicle group, and sending a response data packet to the control center server; after receiving the response data packet, the control center performs comprehensive analysis to determine an unmanned aerial vehicle group for executing tasks; preliminarily planning a conventional path for each unmanned aerial vehicle to be flown, and starting to execute tasks after receiving a starting instruction; the unmanned aerial vehicle cluster and the control center constantly keep a communication state, and the control center carries out full-range scheduling based on particle swarm optimization according to the real-time state of each unmanned aerial vehicle. By utilizing the particle swarm optimization technology, the invention realizes the scheduling intelligence of the unmanned aerial vehicle, improves the scheduling management efficiency of the unmanned aerial vehicle cluster, and effectively reduces the flight energy consumption of the unmanned aerial vehicle when the unmanned aerial vehicle executes tasks.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicle scheduling, in particular to an unmanned aerial vehicle scheduling method and system based on particle swarm optimization and a readable storage medium.
Background
The development history of the unmanned aerial vehicle can trace back to 20 th century, and the unmanned aerial vehicle is mainly used for military training and actual combat at first; after the development of nearly a century, the unmanned aerial vehicle has come into common families, is not only used for military, but also causes strong reverberation in the civil market, and has huge development prospect in the commercial field. With the continuous expansion of the application of the unmanned aerial vehicle, the environmental factors faced by the unmanned aerial vehicle in the flight process are more and more complex, and the types of executed tasks are also diversified; meanwhile, increasingly rich task requirements also urge the generation of a cooperative mode of the unmanned aerial vehicle group. On the other hand, the unmanned aerial vehicle is limited by energy supply, and meanwhile, the unmanned aerial vehicle can be influenced by changing environment and various obstacle types in the flight process, and if the flight is guided by incorrect and efficient scheduling instructions, the task failure can be caused by the problems of 'lost path' or endurance greatly. Therefore, in order to solve the problems existing in the current stage, the scheduling efficiency of the unmanned aerial vehicle needs to be improved, tasks of the unmanned aerial vehicle cluster are scientifically and reasonably distributed, the scheduling intelligence of the unmanned aerial vehicle is promoted, and a flying path is planned to reduce the energy consumption of the unmanned aerial vehicle in the flying process.
Disclosure of Invention
In order to solve at least one technical problem, the invention provides an unmanned aerial vehicle scheduling method and system based on particle swarm optimization and a readable storage medium.
In order to solve the technical problem, the first aspect of the present invention discloses an unmanned aerial vehicle scheduling method based on particle swarm optimization, wherein the method comprises:
the control center sends a task preparation instruction to the standby unmanned aerial vehicle group;
after receiving a task preparation instruction, the unmanned aerial vehicle cluster initializes the parameters of the machine body and sends a response data packet to the control center server;
after receiving the response data packet, the control center performs comprehensive analysis, evaluates the matching degree of the standby unmanned aerial vehicles and the tasks to be executed, determines the unmanned aerial vehicle cluster for executing the tasks, and distributes the tasks for each unmanned aerial vehicle to be flown in a targeted manner;
preliminarily planning a conventional path for each unmanned aerial vehicle to be flown according to the flight mission of the unmanned aerial vehicle;
after the control center sends a starting instruction to the unmanned aerial vehicle group to be flown, the unmanned aerial vehicle group starts to execute a task;
and the unmanned aerial vehicle cluster and the control center constantly keep a communication state in the task execution process, and the control center carries out full-range scheduling based on particle swarm optimization according to the real-time state of each unmanned aerial vehicle.
In this scheme, the response data packet specifically includes: the unique number of unmanned aerial vehicle, current IP, positional information, electric quantity condition, fuselage parameter.
In this scheme, the specific process of performing the comprehensive analysis is as follows:
the control center groups the unmanned aerial vehicles to be commanded according to the use scenes according to the detailed information of the response data packet;
determining the number, the cruising ability and the position information of the unmanned aerial vehicles of each use scene group;
inquiring the flying near conditions and maintenance history of the unmanned aerial vehicles of each use scene group according to the unique number of the unmanned aerial vehicle;
determining the number, state information and task completion degree of unmanned aerial vehicles executing tasks in each usage scene group;
determining the electric quantity condition of the positively-charged unmanned aerial vehicle of each usage scene group;
determining the division task of each group of unmanned aerial vehicles according to the number of tasks, the target position of the tasks and the property of the tasks, and evaluating and grading the task matching degree of each group of unmanned aerial vehicles by combining the comprehensive information of all unmanned aerial vehicles of each group, wherein the task matching degree is divided into a first level, a second level and a third level;
if the evaluation grading is one grade, the unmanned aerial vehicle of the group can normally execute and complete the task;
if the evaluation grading is two grades, the unmanned aerial vehicle of the group can normally execute the task, but needs the support of other unmanned aerial vehicles in the group;
and if the evaluation grading is three grades, the unmanned aerial vehicle of the group cannot normally execute the task and continues to stand by.
In this scheme, control center treats that the unmanned aerial vehicle distribution task is waited to fly for each according to the principle of being close to through calculating and comparing each current position of treating to fly the unmanned aerial vehicle to should treating to fly the straight-line distance between each destination of the group branch task that unmanned aerial vehicle belongs to.
In the scheme, a real-time communication mechanism and a staged communication mechanism are arranged between the unmanned aerial vehicle cluster and the control center; the control center can send a state inspection instruction to any unmanned aerial vehicle at any time, and the inspected unmanned aerial vehicle immediately sends a real-time state data packet to the control center after receiving the instruction; in the process of executing the task, each tenth of the flight distance of the unmanned aerial vehicle group sends a phase state data packet to the control center.
In the scheme, the control center performs path planning based on particle swarm optimization on each unmanned aerial vehicle executing tasks, and the specific steps are as follows:
s1, establishing a path planning mathematical model based on particle swarm optimization;
s2, acquiring a three-dimensional map between the unmanned aerial vehicle and a task destination according to the current position information of the unmanned aerial vehicle and the position information of the distributed task destination;
s3, acquiring coordinate parameters, altitude and spacing distance of the obstacles on the planned path according to the three-dimensional map;
s4, acquiring real-time state data or stage state data of the unmanned aerial vehicle;
s5, inputting state data of the unmanned aerial vehicle, coordinate parameters of the barrier, the altitude and the interval distance into a path planning mathematical model, and solving an optimal path to a destination;
s6, repeating S2 to S5.
In the scheme, all particles in the particle swarm optimization algorithm have velocity vectors and position vectors; the speed vector influences the self searching direction and distance, and the position vector represents the quality of the current solution; in the search domain, each particle follows the particle with the optimal fitness value in the population to search, and the speed and the position of the particle are continuously updated, wherein a specific updating formula is as follows:
wherein: v is velocity and x is position; i represents the ith particle; d is dimension; the optimal position of the individual is piThe global optimum position is pg(ii) a t is the current iteration number; omega is the inertial weight; c1 and c2 are learning factors; r1 and r2 are distributed in [0, 1 ]]A random number within; c1 and r1 bind to the extent to which the attenuated particle is influenced by itself in the iteration, and c2 and r2 bind to the extent to which the attenuated particle is influenced by global optima in the iteration.
The invention discloses an unmanned aerial vehicle dispatching system based on particle swarm optimization, which comprises a memory and a processor, wherein the memory comprises an unmanned aerial vehicle dispatching method program based on particle swarm optimization, and the unmanned aerial vehicle dispatching method program based on particle swarm optimization is executed by the processor to realize the following steps:
the control center sends a task preparation instruction to the standby unmanned aerial vehicle group;
after receiving a task preparation instruction, the unmanned aerial vehicle cluster initializes the parameters of the machine body and sends a response data packet to the control center server;
after receiving the response data packet, the control center performs comprehensive analysis, evaluates the matching degree of the standby unmanned aerial vehicles and the tasks to be executed, determines the unmanned aerial vehicle cluster for executing the tasks, and distributes the tasks for each unmanned aerial vehicle to be flown in a targeted manner;
preliminarily planning a conventional path for each unmanned aerial vehicle to be flown according to the flight mission of the unmanned aerial vehicle;
after the control center sends a starting instruction to the unmanned aerial vehicle group to be flown, the unmanned aerial vehicle group starts to execute a task;
and the unmanned aerial vehicle cluster and the control center constantly keep a communication state in the task execution process, and the control center carries out full-range scheduling based on particle swarm optimization according to the real-time state of each unmanned aerial vehicle.
In this scheme, the response data packet specifically includes: the unique number of unmanned aerial vehicle, current IP, positional information, electric quantity condition, fuselage parameter.
In this scheme, the specific process of performing the comprehensive analysis is as follows:
the control center groups the unmanned aerial vehicles to be commanded according to the use scenes according to the detailed information of the response data packet;
determining the number, the cruising ability and the position information of the unmanned aerial vehicles of each use scene group;
inquiring the flying near conditions and maintenance history of the unmanned aerial vehicles of each use scene group according to the unique number of the unmanned aerial vehicle;
determining the number, state information and task completion degree of unmanned aerial vehicles executing tasks in each usage scene group;
determining the electric quantity condition of the positively-charged unmanned aerial vehicle of each usage scene group;
determining the division task of each group of unmanned aerial vehicles according to the number of tasks, the target position of the tasks and the property of the tasks, and evaluating and grading the task matching degree of each group of unmanned aerial vehicles by combining the comprehensive information of all unmanned aerial vehicles of each group, wherein the task matching degree is divided into a first level, a second level and a third level;
if the evaluation grading is one grade, the unmanned aerial vehicle of the group can normally execute and complete the task;
if the evaluation grading is two grades, the unmanned aerial vehicle of the group can normally execute the task, but needs the support of other unmanned aerial vehicles in the group;
and if the evaluation grading is three grades, the unmanned aerial vehicle of the group cannot normally execute the task and continues to stand by.
In this scheme, control center treats that the unmanned aerial vehicle distribution task is waited to fly for each according to the principle of being close to through calculating and comparing each current position of treating to fly the unmanned aerial vehicle to should treating to fly the straight-line distance between each destination of the group branch task that unmanned aerial vehicle belongs to.
In the scheme, a real-time communication mechanism and a staged communication mechanism are arranged between the unmanned aerial vehicle cluster and the control center; the control center can send a state inspection instruction to any unmanned aerial vehicle at any time, and the inspected unmanned aerial vehicle immediately sends a real-time state data packet to the control center after receiving the instruction; in the process of executing the task, each tenth of the flight distance of the unmanned aerial vehicle group sends a phase state data packet to the control center.
In the scheme, the control center performs path planning based on particle swarm optimization on each unmanned aerial vehicle executing tasks, and the specific steps are as follows:
s1, establishing a path planning mathematical model based on particle swarm optimization;
s2, acquiring a three-dimensional map between the unmanned aerial vehicle and a task destination according to the current position information of the unmanned aerial vehicle and the position information of the distributed task destination;
s3, acquiring coordinate parameters, altitude and spacing distance of the obstacles on the planned path according to the three-dimensional map;
s4, acquiring real-time state data or stage state data of the unmanned aerial vehicle;
s5, inputting state data of the unmanned aerial vehicle, coordinate parameters of the barrier, the altitude and the interval distance into a path planning mathematical model, and solving an optimal path to a destination;
s6, repeating S2 to S5.
In the scheme, all particles in the particle swarm optimization algorithm have velocity vectors and position vectors; the speed vector influences the self searching direction and distance, and the position vector represents the quality of the current solution; in the search domain, each particle follows the particle with the optimal fitness value in the population to search, and the speed and the position of the particle are continuously updated, wherein a specific updating formula is as follows:
wherein: v is velocity and x is position; i represents the ith particle; d is dimension; the optimal position of the individual is piThe global optimum position is pg(ii) a t is the current iteration number; omega is the inertial weight; c1 and c2 are learning factors; r1 and r2 are distributed in [0, 1 ]]A random number within; c1 and r1 bind to the extent to which the attenuated particle is influenced by itself in the iteration, and c2 and r2 bind to the extent to which the attenuated particle is influenced by global optima in the iteration.
In the scheme, the unmanned aerial vehicle dispatching system comprises a data receiving module, a data analysis processing module, a path planning module and an instruction dispatching module.
The invention discloses a third aspect of the computer readable storage medium, the computer readable storage medium comprises a particle swarm optimization-based unmanned aerial vehicle scheduling method program of a machine, and when the particle swarm optimization-based unmanned aerial vehicle scheduling method program is executed by a processor, the steps of the particle swarm optimization-based unmanned aerial vehicle scheduling method are realized.
According to the unmanned aerial vehicle scheduling method and system based on particle swarm optimization and the readable storage medium, the particle swarm optimization technology is utilized, so that the unmanned aerial vehicle scheduling intelligence is realized, the scheduling management efficiency of the unmanned aerial vehicle cluster is improved, and the flight energy consumption of the unmanned aerial vehicle during task execution is effectively reduced.
Drawings
FIG. 1 shows a flow chart of an unmanned aerial vehicle scheduling method based on particle swarm optimization;
fig. 2 shows a block diagram of the unmanned aerial vehicle dispatching system based on particle swarm optimization.
Detailed description of the invention
In order that the above objects, features and advantages of the present invention can be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings. It should be noted that the embodiments and features of the embodiments of the present application may be combined with each other without conflict.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those specifically described herein, and therefore the scope of the present invention is not limited by the specific embodiments disclosed below.
Fig. 1 shows a flow chart of an unmanned aerial vehicle scheduling method based on particle swarm optimization.
As shown in fig. 1, a first aspect of the present invention discloses an unmanned aerial vehicle scheduling method based on particle swarm optimization, which includes:
the control center sends a task preparation instruction to the standby unmanned aerial vehicle group;
after receiving a task preparation instruction, the unmanned aerial vehicle cluster initializes the parameters of the machine body and sends a response data packet to the control center server;
after receiving the response data packet, the control center performs comprehensive analysis, evaluates the matching degree of the standby unmanned aerial vehicles and the tasks to be executed, determines the unmanned aerial vehicle cluster for executing the tasks, and distributes the tasks for each unmanned aerial vehicle to be flown in a targeted manner;
preliminarily planning a conventional path for each unmanned aerial vehicle to be flown according to the flight mission of the unmanned aerial vehicle;
after the control center sends a starting instruction to the unmanned aerial vehicle group to be flown, the unmanned aerial vehicle group starts to execute a task;
and the unmanned aerial vehicle cluster and the control center constantly keep a communication state in the task execution process, and the control center carries out full-range scheduling based on particle swarm optimization according to the real-time state of each unmanned aerial vehicle.
It should be noted that, the unmanned aerial vehicle of the present invention includes a communication unit, which is used for maintaining communication with the control center, receiving various commands and sending various data packets; the unmanned aerial vehicle positioning system comprises a GPS positioning unit, a positioning unit and a positioning unit, wherein the GPS positioning unit is used for acquiring real-time position information of the unmanned aerial vehicle; the unmanned aerial vehicle control system comprises a flight control unit and is used for analyzing instructions of a control center, controlling the flight speed and the flight course of the unmanned aerial vehicle, actively avoiding obstacles and the like. Wherein the communication unit and the GPS positioning unit are electrically connected with the flight control unit.
It should be noted that, in the invention, the unmanned aerial vehicle sends a return request to the control center after completing the task, and reports the current state data; after confirming that the task is completed, the control center verifies the current state of the unmanned aerial vehicle; and if the requirement of returning or continuing to execute the next task is met, sending a returning instruction or a task preparation instruction to the unmanned aerial vehicle, otherwise, sending an in-situ standby instruction.
In this scheme, the response data packet specifically includes: the unique number of unmanned aerial vehicle, current IP, positional information, electric quantity condition, fuselage parameter.
In this scheme, the specific process of performing the comprehensive analysis is as follows:
the control center groups the unmanned aerial vehicles to be commanded according to the use scenes according to the detailed information of the response data packet;
determining the number, the cruising ability and the position information of the unmanned aerial vehicles of each use scene group;
inquiring the flying near conditions and maintenance history of the unmanned aerial vehicles of each use scene group according to the unique number of the unmanned aerial vehicle;
determining the number, state information and task completion degree of unmanned aerial vehicles executing tasks in each usage scene group;
determining the electric quantity condition of the positively-charged unmanned aerial vehicle of each usage scene group;
determining the division task of each group of unmanned aerial vehicles according to the number of tasks, the target position of the tasks and the property of the tasks, and evaluating and grading the task matching degree of each group of unmanned aerial vehicles by combining the comprehensive information of all unmanned aerial vehicles of each group, wherein the task matching degree is divided into a first level, a second level and a third level;
if the evaluation grading is one grade, the unmanned aerial vehicle of the group can normally execute and complete the task;
if the evaluation grading is two grades, the unmanned aerial vehicle of the group can normally execute the task, but needs the support of other unmanned aerial vehicles in the group;
and if the evaluation grading is three grades, the unmanned aerial vehicle of the group cannot normally execute the task and continues to stand by.
It should be noted that, when the evaluation level is set as the second level, the group of standby unmanned aerial vehicles first performs a part of the task of dividing work, and the rest of the standby unmanned aerial vehicles are completed within the effective task time limit after the other unmanned aerial vehicles in the group perform the task or the charging is completed.
In this scheme, control center treats that the unmanned aerial vehicle distribution task is waited to fly for each according to the principle of being close to through calculating and comparing each current position of treating to fly the unmanned aerial vehicle to should treating to fly the straight-line distance between each destination of the group branch task that unmanned aerial vehicle belongs to.
It should be noted that, according to the actual mission planning requirement, the to-be-flown unmanned aerial vehicle in the present invention may be allocated with more than one mission under the condition of permission of cruising ability.
In the scheme, a real-time communication mechanism and a staged communication mechanism are arranged between the unmanned aerial vehicle cluster and the control center; the control center can send a state inspection instruction to any unmanned aerial vehicle at any time, and the inspected unmanned aerial vehicle immediately sends a real-time state data packet to the control center after receiving the instruction; in the process of executing the task, each tenth of the flight distance of the unmanned aerial vehicle group sends a phase state data packet to the control center.
It should be noted that the information included in the status data packet includes: flight direction, flight speed, altitude, current position, residual electricity, energy consumption condition, wind speed, fuselage temperature, ambient humidity, atmospheric pressure.
In the scheme, the control center performs path planning based on particle swarm optimization on each unmanned aerial vehicle executing tasks, and the specific steps are as follows:
s1, establishing a path planning mathematical model based on particle swarm optimization;
s2, acquiring a three-dimensional map between the unmanned aerial vehicle and a task destination according to the current position information of the unmanned aerial vehicle and the position information of the distributed task destination;
s3, acquiring coordinate parameters, altitude and spacing distance of the obstacles on the planned path according to the three-dimensional map;
s4, acquiring real-time state data or stage state data of the unmanned aerial vehicle;
s5, inputting state data of the unmanned aerial vehicle, coordinate parameters of the barrier, the altitude and the interval distance into a path planning mathematical model, and solving an optimal path to a destination;
s6, repeating S2 to S5.
It should be noted that, in the invention, the control center performs path planning based on particle swarm optimization once every time it receives a periodic state data packet; an operator can selectively plan a specified path for any unmanned aerial vehicle according to personal experience judgment and comprehensive consideration; meanwhile, an operator can manually plan a path based on particle swarm optimization at any time according to personal wishes.
In the scheme, all particles in the particle swarm optimization algorithm have velocity vectors and position vectors; the speed vector influences the self searching direction and distance, and the position vector represents the quality of the current solution; in the search domain, each particle follows the particle with the optimal fitness value in the population to search, and the speed and the position of the particle are continuously updated, wherein a specific updating formula is as follows:
wherein: v is velocity and x is position; i represents the ith particle; d is dimension; the optimal position of the individual is piThe global optimum position is pg(ii) a t is the current iteration number; omega is the inertial weight; c1 and c2 are learning factors; r1 and r2 are distributed in [0, 1 ]]A random number within; c1 and r1 bind to the extent to which the attenuated particle is influenced by itself in the iteration, and c2 and r2 bind to the extent to which the attenuated particle is influenced by global optima in the iteration.
It should be noted that the particle swarm optimization algorithm described in the present invention can increase the number of iterations according to the size of the particle swarm, avoid falling into the local optimum, and ensure to obtain the global optimum solution in the solution space.
Fig. 2 shows a block diagram of the unmanned aerial vehicle dispatching system based on particle swarm optimization.
As shown in fig. 2, a second aspect of the present invention discloses an unmanned aerial vehicle scheduling system optimized based on a particle swarm optimization, which includes a memory and a processor, wherein the memory includes an unmanned aerial vehicle scheduling method program optimized based on the particle swarm optimization, and when being executed by the processor, the unmanned aerial vehicle scheduling method program optimized based on the particle swarm optimization realizes the following steps:
the control center sends a task preparation instruction to the standby unmanned aerial vehicle group;
after receiving a task preparation instruction, the unmanned aerial vehicle cluster initializes the parameters of the machine body and sends a response data packet to the control center server;
after receiving the response data packet, the control center performs comprehensive analysis, evaluates the matching degree of the standby unmanned aerial vehicles and the tasks to be executed, determines the unmanned aerial vehicle cluster for executing the tasks, and distributes the tasks for each unmanned aerial vehicle to be flown in a targeted manner;
preliminarily planning a conventional path for each unmanned aerial vehicle to be flown according to the flight mission of the unmanned aerial vehicle;
after the control center sends a starting instruction to the unmanned aerial vehicle group to be flown, the unmanned aerial vehicle group starts to execute a task;
and the unmanned aerial vehicle cluster and the control center constantly keep a communication state in the task execution process, and the control center carries out full-range scheduling based on particle swarm optimization according to the real-time state of each unmanned aerial vehicle.
It should be noted that, the unmanned aerial vehicle of the present invention includes a communication unit, which is used for maintaining communication with the control center, receiving various commands and sending various data packets; the unmanned aerial vehicle positioning system comprises a GPS positioning unit, a positioning unit and a positioning unit, wherein the GPS positioning unit is used for acquiring real-time position information of the unmanned aerial vehicle; the unmanned aerial vehicle control system comprises a flight control unit and is used for analyzing instructions of a control center, controlling the flight speed and the flight course of the unmanned aerial vehicle, actively avoiding obstacles and the like. Wherein the communication unit and the GPS positioning unit are electrically connected with the flight control unit.
It should be noted that, in the invention, the unmanned aerial vehicle sends a return request to the control center after completing the task, and reports the current state data; after confirming that the task is completed, the control center verifies the current state of the unmanned aerial vehicle; and if the requirement of returning or continuing to execute the next task is met, sending a returning instruction or a task preparation instruction to the unmanned aerial vehicle, otherwise, sending an in-situ standby instruction.
It should be noted that the system of the present invention can be operated in a terminal device such as a server, a PC, a mobile phone, a PAD, and the like.
It should be noted that the processor may be a Central Processing Unit (CPU), other general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, and so on. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
In this scheme, the response data packet specifically includes: the unique number of unmanned aerial vehicle, current IP, positional information, electric quantity condition, fuselage parameter.
In this scheme, the specific process of performing the comprehensive analysis is as follows:
the control center groups the unmanned aerial vehicles to be commanded according to the use scenes according to the detailed information of the response data packet;
determining the number, the cruising ability and the position information of the unmanned aerial vehicles of each use scene group;
inquiring the flying near conditions and maintenance history of the unmanned aerial vehicles of each use scene group according to the unique number of the unmanned aerial vehicle;
determining the number, state information and task completion degree of unmanned aerial vehicles executing tasks in each usage scene group;
determining the electric quantity condition of the positively-charged unmanned aerial vehicle of each usage scene group;
determining the division task of each group of unmanned aerial vehicles according to the number of tasks, the target position of the tasks and the property of the tasks, and evaluating and grading the task matching degree of each group of unmanned aerial vehicles by combining the comprehensive information of all unmanned aerial vehicles of each group, wherein the task matching degree is divided into a first level, a second level and a third level;
if the evaluation grading is one grade, the unmanned aerial vehicle of the group can normally execute and complete the task;
if the evaluation grading is two grades, the unmanned aerial vehicle of the group can normally execute the task, but needs the support of other unmanned aerial vehicles in the group;
and if the evaluation grading is three grades, the unmanned aerial vehicle of the group cannot normally execute the task and continues to stand by.
It should be noted that, when the evaluation level is set as the second level, the group of standby unmanned aerial vehicles first performs a part of the task of dividing work, and the rest of the standby unmanned aerial vehicles are completed within the effective task time limit after the other unmanned aerial vehicles in the group perform the task or the charging is completed.
In this scheme, control center treats that the unmanned aerial vehicle distribution task is waited to fly for each according to the principle of being close to through calculating and comparing each current position of treating to fly the unmanned aerial vehicle to should treating to fly the straight-line distance between each destination of the group branch task that unmanned aerial vehicle belongs to.
It should be noted that, according to the actual mission planning requirement, the to-be-flown unmanned aerial vehicle in the present invention may be allocated with more than one mission under the condition of permission of cruising ability.
In the scheme, a real-time communication mechanism and a staged communication mechanism are arranged between the unmanned aerial vehicle cluster and the control center; the control center can send a state inspection instruction to any unmanned aerial vehicle at any time, and the inspected unmanned aerial vehicle immediately sends a real-time state data packet to the control center after receiving the instruction; in the process of executing the task, each tenth of the flight distance of the unmanned aerial vehicle group sends a phase state data packet to the control center.
It should be noted that the information included in the status data packet includes: flight direction, flight speed, altitude, current position, residual electricity, energy consumption condition, wind speed, fuselage temperature, ambient humidity, atmospheric pressure.
In the scheme, the control center performs path planning based on particle swarm optimization on each unmanned aerial vehicle executing tasks, and the specific steps are as follows:
s1, establishing a path planning mathematical model based on particle swarm optimization;
s2, acquiring a three-dimensional map between the unmanned aerial vehicle and a task destination according to the current position information of the unmanned aerial vehicle and the position information of the distributed task destination;
s3, acquiring coordinate parameters, altitude and spacing distance of the obstacles on the planned path according to the three-dimensional map;
s4, acquiring real-time state data or stage state data of the unmanned aerial vehicle;
s5, inputting state data of the unmanned aerial vehicle, coordinate parameters of the barrier, the altitude and the interval distance into a path planning mathematical model, and solving an optimal path to a destination;
s6, repeating S2 to S5.
It should be noted that, in the invention, the control center performs path planning based on particle swarm optimization once every time it receives a periodic state data packet; an operator can selectively plan a specified path for any unmanned aerial vehicle according to personal experience judgment and comprehensive consideration; meanwhile, an operator can manually plan a path based on particle swarm optimization at any time according to personal wishes.
In the scheme, all particles in the particle swarm optimization algorithm have velocity vectors and position vectors; the speed vector influences the self searching direction and distance, and the position vector represents the quality of the current solution; in the search domain, each particle follows the particle with the optimal fitness value in the population to search, and the speed and the position of the particle are continuously updated, wherein a specific updating formula is as follows:
wherein: v is velocity and x is position; i represents the ith particle; d is dimension; the optimal position of the individual is piThe global optimum position is pg(ii) a t is the current iteration number; omega is the inertial weight; c1 and c2 are learning factors; r1 and r2 are distributed in [0, 1 ]]A random number within; c1 and r1 bind to the extent to which the attenuated particle is influenced by itself in the iteration, and c2 and r2 bind to the extent to which the attenuated particle is influenced by global optima in the iteration.
It should be noted that the particle swarm optimization algorithm described in the present invention can increase the number of iterations according to the size of the particle swarm, avoid falling into the local optimum, and ensure to obtain the global optimum solution in the solution space.
In the scheme, the unmanned aerial vehicle dispatching system comprises a data receiving module, a data analysis processing module, a path planning module and an instruction dispatching module.
The invention discloses a third aspect of the computer readable storage medium, the computer readable storage medium comprises a particle swarm optimization-based unmanned aerial vehicle scheduling method program of a machine, and when the particle swarm optimization-based unmanned aerial vehicle scheduling method program is executed by a processor, the steps of the particle swarm optimization-based unmanned aerial vehicle scheduling method are realized.
According to the unmanned aerial vehicle scheduling method and system based on particle swarm optimization and the readable storage medium, the particle swarm optimization technology is utilized, so that the unmanned aerial vehicle scheduling intelligence is realized, the scheduling management efficiency of the unmanned aerial vehicle cluster is improved, and the flight energy consumption of the unmanned aerial vehicle during task execution is effectively reduced.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described device embodiments are merely illustrative, for example, the division of the unit is only a logical functional division, and there may be other division ways in actual implementation, such as: multiple units or components may be combined, or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the coupling, direct coupling or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between the devices or units may be electrical, mechanical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units; can be located in one place or distributed on a plurality of network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, all the functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately regarded as one unit, or two or more units may be integrated into one unit; the integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
Those of ordinary skill in the art will understand that: all or part of the steps for realizing the method embodiments can be completed by hardware related to program instructions, the program can be stored in a computer readable storage medium, and the program executes the steps comprising the method embodiments when executed; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Alternatively, the integrated unit of the present invention may be stored in a computer-readable storage medium if it is implemented in the form of a software functional module and sold or used as a separate product. Based on such understanding, the technical solutions of the embodiments of the present invention may be essentially implemented or a part contributing to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, a ROM, a RAM, a magnetic or optical disk, or various other media that can store program code.
Claims (10)
1. An unmanned aerial vehicle scheduling method based on particle swarm optimization is characterized by comprising the following steps:
the control center sends a task preparation instruction to the standby unmanned aerial vehicle group;
after receiving a task preparation instruction, the unmanned aerial vehicle cluster initializes the parameters of the machine body and sends a response data packet to the control center server;
after receiving the response data packet, the control center performs comprehensive analysis, evaluates the matching degree of the standby unmanned aerial vehicles and the tasks to be executed, determines the unmanned aerial vehicle cluster for executing the tasks, and distributes the tasks for each unmanned aerial vehicle to be flown in a targeted manner;
preliminarily planning a conventional path for each unmanned aerial vehicle to be flown according to the flight mission of the unmanned aerial vehicle;
after the control center sends a starting instruction to the unmanned aerial vehicle group to be flown, the unmanned aerial vehicle group starts to execute a task;
and the unmanned aerial vehicle cluster and the control center constantly keep a communication state in the task execution process, and the control center carries out full-range scheduling based on particle swarm optimization according to the real-time state of each unmanned aerial vehicle.
2. The particle swarm optimization-based unmanned aerial vehicle scheduling method according to claim 1, wherein the response data packet specifically comprises: the unique number of unmanned aerial vehicle, current IP, positional information, electric quantity condition, fuselage parameter.
3. The unmanned aerial vehicle dispatching method based on particle swarm optimization according to claim 1, wherein the specific process of performing the comprehensive analysis is as follows:
the control center groups the unmanned aerial vehicles to be commanded according to the use scenes according to the detailed information of the response data packet;
determining the number, the cruising ability and the position information of the unmanned aerial vehicles of each use scene group;
inquiring the flying near conditions and maintenance history of the unmanned aerial vehicles of each use scene group according to the unique number of the unmanned aerial vehicle;
determining the number, state information and task completion degree of unmanned aerial vehicles executing tasks in each usage scene group;
determining the electric quantity condition of the positively-charged unmanned aerial vehicle of each usage scene group;
determining the division task of each group of unmanned aerial vehicles according to the number of tasks, the target position of the tasks and the property of the tasks, and evaluating and grading the task matching degree of each group of unmanned aerial vehicles by combining the comprehensive information of all unmanned aerial vehicles of each group, wherein the task matching degree is divided into a first level, a second level and a third level;
if the evaluation grading is one grade, the unmanned aerial vehicle of the group can normally execute and complete the task;
if the evaluation grading is two grades, the unmanned aerial vehicle of the group can normally execute the task, but needs the support of other unmanned aerial vehicles in the group;
and if the evaluation grading is three grades, the unmanned aerial vehicle of the group cannot normally execute the task and continues to stand by.
4. The unmanned aerial vehicle scheduling method based on particle swarm optimization according to claim 1, wherein the control center allocates tasks to each unmanned aerial vehicle to be flown according to the principle of proximity by calculating and comparing the linear distance between the current position of each unmanned aerial vehicle to be flown and each destination of the task of the group in which the unmanned aerial vehicle to be flown is located.
5. The unmanned aerial vehicle scheduling method based on particle swarm optimization according to claim 1, wherein a real-time communication mechanism and a staged communication mechanism are arranged between the unmanned aerial vehicle cluster and the control center; the control center can send a state inspection instruction to any unmanned aerial vehicle at any time, and the inspected unmanned aerial vehicle immediately sends a real-time state data packet to the control center after receiving the instruction; in the process of executing the task, each tenth of the flight distance of the unmanned aerial vehicle group sends a phase state data packet to the control center.
6. The method for dispatching the unmanned aerial vehicles based on particle swarm optimization according to claim 1, wherein the control center performs path planning based on particle swarm optimization on each unmanned aerial vehicle executing tasks, and the specific steps are as follows:
s1, establishing a path planning mathematical model based on particle swarm optimization;
s2, acquiring a three-dimensional map between the unmanned aerial vehicle and a task destination according to the current position information of the unmanned aerial vehicle and the position information of the distributed task destination;
s3, acquiring coordinate parameters, altitude and spacing distance of the obstacles on the planned path according to the three-dimensional map;
s4, acquiring real-time state data or stage state data of the unmanned aerial vehicle;
s5, inputting state data of the unmanned aerial vehicle, coordinate parameters of the barrier, the altitude and the interval distance into a path planning mathematical model, and solving an optimal path to a destination;
s6, repeating S2 to S5.
7. The unmanned aerial vehicle scheduling method based on particle swarm optimization according to claim 1, wherein all particles in the particle swarm optimization have velocity vectors and position vectors; the speed vector influences the self searching direction and distance, and the position vector represents the quality of the current solution; in the search domain, each particle follows the particle with the optimal fitness value in the population to search, and the speed and the position of the particle are continuously updated, wherein a specific updating formula is as follows:
wherein: v is velocity and x is position; i represents the ith particle; d is dimension; the optimal position of the individual is piThe global optimum position is pg(ii) a t is the current iteration number; omega is the inertial weight; c1 and c2 are learning factors; r1 and r2 are distributed in [0, 1 ]]A random number within; c1 and r1 bind to the extent to which the attenuated particle is influenced by itself in the iteration, and c2 and r2 bind to the extent to which the attenuated particle is influenced by global optima in the iteration.
8. The unmanned aerial vehicle dispatching system based on particle swarm optimization is characterized by comprising a memory and a processor, wherein the memory comprises an unmanned aerial vehicle dispatching method program based on particle swarm optimization, and the unmanned aerial vehicle dispatching method program based on particle swarm optimization is executed by the processor to realize the following steps:
the control center sends a task preparation instruction to the standby unmanned aerial vehicle group;
after receiving a task preparation instruction, the unmanned aerial vehicle cluster initializes the parameters of the machine body and sends a response data packet to the control center server;
after receiving the response data packet, the control center performs comprehensive analysis, evaluates the matching degree of the standby unmanned aerial vehicles and the tasks to be executed, determines the unmanned aerial vehicle cluster for executing the tasks, and distributes the tasks for each unmanned aerial vehicle to be flown in a targeted manner;
preliminarily planning a conventional path for each unmanned aerial vehicle to be flown according to the flight mission of the unmanned aerial vehicle;
after the control center sends a starting instruction to the unmanned aerial vehicle group to be flown, the unmanned aerial vehicle group starts to execute a task;
and the unmanned aerial vehicle cluster and the control center constantly keep a communication state in the task execution process, and the control center carries out full-range scheduling based on particle swarm optimization according to the real-time state of each unmanned aerial vehicle.
9. The particle swarm optimization-based unmanned aerial vehicle dispatching system according to claim 8, comprising a data receiving module, a data analysis processing module, a path planning module and an instruction dispatching module.
10. A computer-readable storage medium, wherein the computer-readable storage medium includes a program of a particle swarm optimization-based unmanned aerial vehicle scheduling method for a machine, and when the program of the particle swarm optimization-based unmanned aerial vehicle scheduling method is executed by a processor, the steps of the particle swarm optimization-based unmanned aerial vehicle scheduling method according to any one of claims 1 to 7 are implemented.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113064449A (en) * | 2021-03-31 | 2021-07-02 | 广东电网有限责任公司电力调度控制中心 | Unmanned aerial vehicle scheduling method and system |
| CN114326805A (en) * | 2021-12-28 | 2022-04-12 | 广东电网有限责任公司 | Unmanned aerial vehicle overhead line inspection cooperative control method and system and storage medium |
| CN115187005A (en) * | 2022-06-23 | 2022-10-14 | 中国人民公安大学 | Scheduling method, device, equipment and storage medium |
| TWI814544B (en) * | 2022-08-18 | 2023-09-01 | 中華電信股份有限公司 | Electronic device and method for generating optimal flight trajectory based on flying point of unmanned aerial vehicle |
| CN116679758A (en) * | 2023-08-04 | 2023-09-01 | 江西现代职业技术学院 | Unmanned aerial vehicle scheduling method, unmanned aerial vehicle scheduling system, computer and readable storage medium |
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2020
- 2020-05-28 CN CN202010481908.4A patent/CN111580561A/en not_active Withdrawn
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113064449A (en) * | 2021-03-31 | 2021-07-02 | 广东电网有限责任公司电力调度控制中心 | Unmanned aerial vehicle scheduling method and system |
| CN113064449B (en) * | 2021-03-31 | 2024-03-12 | 广东电网有限责任公司电力调度控制中心 | Unmanned aerial vehicle scheduling method and system |
| CN114326805A (en) * | 2021-12-28 | 2022-04-12 | 广东电网有限责任公司 | Unmanned aerial vehicle overhead line inspection cooperative control method and system and storage medium |
| CN115187005A (en) * | 2022-06-23 | 2022-10-14 | 中国人民公安大学 | Scheduling method, device, equipment and storage medium |
| TWI814544B (en) * | 2022-08-18 | 2023-09-01 | 中華電信股份有限公司 | Electronic device and method for generating optimal flight trajectory based on flying point of unmanned aerial vehicle |
| CN116679758A (en) * | 2023-08-04 | 2023-09-01 | 江西现代职业技术学院 | Unmanned aerial vehicle scheduling method, unmanned aerial vehicle scheduling system, computer and readable storage medium |
| CN116679758B (en) * | 2023-08-04 | 2023-10-17 | 江西现代职业技术学院 | Unmanned aerial vehicle scheduling method, unmanned aerial vehicle scheduling system, computer and readable storage medium |
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